As data lakes grow, it becomes harder to analyze and glean the insights from the massive amount of data inside them. With global data volume projected to reach 175 zettabytes by 2025, that’s no small challenge. Data lakes can quickly become data swamps, where the data is more challenging to find and identify as volume scales upward.
For data center operators, this is unwieldy, time-consuming, and costly. Teams may not be able to find what they need — and they might not even know where to look in the first place. For the end-user, valuable insights may not be found in the swamp — insights that could seminally impact the task at hand, be it medical research, financial transactions, retail reports or simply running ecommerce systems more efficiently.
Traditionally, teams created data warehouses using database management systems. In addition, since many databases were not well suited for unstructured data, a separate file system repository might also be used to associate related files, images, logs and other big data. Unfortunately, this burdened data center operators with needing to manage two data repositories and keep them in sync as data changes.
Teams too often prioritize the fit and